Optimal Decision Making for Hybrid Power Systems Selection with Smart Grid and Local Generator Using Wireless Sensor Networks

Home Energy Management System (HEMS) at smart homes focuses on smart management and switching among different sources of energy supply and generations. Wireless Sensor Networks (WSNs) are employed in HEMS to sense, process, and communicate the different electrical parameters, such as electrical voltage, current, and frequency. The sources of electrical energy considered in this paper are the utility smart grid and the local power generator. In this paper, Hybrid Power Systems Selection (HPSS) is developed to ubiquitously distribute the demands of the electrical power between the utility smart grid and the local generator using WSNs, so that the energy cost and pollutant emission are reduced. Therefore, the main goal of HPSS is to get the optimal percentage of electrical power drifted from the smart grid utility, compared with the electrical power drifted from the local generator (UGR). At each time step, HPSS firstly measures the electrical parameters, such as the load power, and the grid frequency. After that, the cost function that combines the energy cost and emission pollution is solved to get the optimal solution. Simulated annealing optimization method is used to get the optimal/near-optimal solution of power distribution. Simulation results show that the proposed HPSS can optimally distribute the load power between the local generator and the utility smart grid with minimum energy cost and pollutant emission. In addition, the energy cost and emission pollution are controlled according to tuning of predefined weighting parameter.